This dataset contains land surface soil moisture products with SMAP time-expanded daily 0.25°×0.25°in Qinghai-Tibet Plateau Area. The dataset was produced based on the Random Forest method by utilizing passive microwave brightness temperature along with some auxiliary datasets. The temporal resolution of the product in 1980,1985,1990,1995 and 2000 is monthly, by using SMMR, SSM/I, and SSMIS brightness temperature from 19 GHz V/H and 37 GHz V channels. The temporal resolution of the product between June 20, 2002 and Dec 30, 2018 is daily, by utilizing AMSR-E and AMSR2 brightness temperature from 6.925 GHz V/H, 10.65 GHz V/H, and 36.5 GHz V channels. The auxiliary datasets participating in the Random Forest training include the IGBP land cover type, GTOPO30 DEM, and Lat/Lon information.
CHAI Linna, ZHU Zhongli, LIU Shaomin
The data set contains the monthly net primary productivity data of 2012-2015. The data is based on the temperature, precipitation, solar radiation and other climatic elements of the daily value data set of China's surface climate data, as well as the data of evapotranspiration et, potential PET, photosynthetic effective absorption ratio FPAR, NDVI and maximum light utilization rate, which are calculated by CASA model. The calculation results are verified by the data of Sanjiangyuan sampling point, The correlation coefficient is 0.718. The data set can be directly used for the analysis of grassland vegetation change in the Qinghai Tibet Plateau, providing the basis for dynamic monitoring of grassland change, and for the management of Grassland Change in the Qinghai Tibet Plateau.
FAN Jiangwen, XIN Liangjie, ZHANG Haiyan, YUAN Xiu
The strong spatial and temporal changes of precipitation often make it impossible to accurately know the spatial distribution and intensity changes of precipitation during the precipitation observation of conventional foundation stations. Satellite microwave remote sensing can overcome this limitation and achieve global scale precipitation and cloud observation. Compared with infrared/visible light, which can only reflect cloud thickness and cloud height, microwave can penetrate the cloud, and also use the interaction between precipitation and cloud particles in the cloud and microwave to detect the cloud and rain more directly. This data use the surface precipitation, obtained by the DPR double wave band precipitation radar carried by GPM, as the true value, soil temperature/humidity of NDVI, DEM and ERA5 as reference data. And the multi-band passive brightness temperature data of GMI is used to invert the instantaneous precipitation intensity during the warm season (May-September) in Tibetan Plateau, then the result is re-sampled to the spatial resolution of 0.1°and accumulated them to a day.
XU Shiguang
Soil data are extremely important at both global and local scales, and in the absence of reliable soil data, land degradation assessments, environmental impact studies and sustainable land management interventions are severely hampered。By Soil information data in the urgent need of the World, especially under the background of the convention on climate change, international institute for applied systems analysis (IIASA) and the UN food and agriculture organization (FAO) and the Kyoto protocol on Soil carbon measurement and the United Nations food and agriculture organization (FAO)/international global agriculture ecological assessment (GAEZ v3.0) jointly established under the sponsorship of a new generation of World Soil Database (Harmonized World Soil Database version 1.2) (HWSD V1.2). The 2010 data set of soil texture on the qinghai-tibet plateau was culled from the world soil database.Data format :grid format, projected as WGS84.The main soil classification system used is fao-90.Unique verification identifier of core soil institution unit: Mu_global-hwsd database soil mapping unit identifier that connects GIS layers. MU_SOURCE1 and MU_SOURCE2- source database mapping unit identifiers； SEQ- soil unit sequence in the composition of soil mapping unit; Soil classification system USES fao-7 classification system or fao-90 classification system (SU_SYM74 resp.su_sym90) or fao-85 (SU_SYM85). The main fields of the soil property sheet include: ID(database ID) MU_GLOBAL(soil unit identifier) (global) SU_SYMBOL Soil mapping unit SU_SYM74(FAO74classify ); SU_SYM85(FAO85classify); SU_SYM90（FAO90The soil name in a soil classification system)； SU_CODE Soil mapping unit code SU_CODE74 Soil unit name SU_CODE85 Soil unit name SU_CODE90 Soil unit name DRAINAGE(19.5); REF_DEPTH(Soil reference depth); AWC_CLASS(19.5); AWC_CLASS(Soil available water content); PHASE1: Real (The soil phase); PHASE2: String (The soil phase); ROOTS: String (Depth classification of obstacles to the bottom of the soil)； SWR: String (Characteristics of soil moisture content)； ADD_PROP: Real (A specific soil type in a soil unit that is associated with agricultural use)； T_TEXTURE(Topsoil texture); T_GRAVEL: Real (Percentage of aggregate volume on top)；( unit：%vol.) T_SAND: Real (Top sand content)； ( unit：% wt.) T_SILT: Real (surface silt content);(unit: % wt.) T_CLAY: Real (clay content on top);(unit: % wt.) T_USDA_TEX: Real (top-level USDA soil texture classification);(unit: name) T_REF_BULK: Real (top soil bulk density);(unit: kg/dm3.) T_OC: Real (top organic carbon content);(unit: % weight) T_PH_H2O: Real (top ph) (unit: -log(H+)) T_CEC_CLAY: Real (the cationic exchange capacity of the clay layer at the top);(unit: cmol/kg) T_CEC_SOIL: Real (cation exchange capacity of topsoil) (unit: cmol/kg) T_BS: Real (top basic saturation);(unit: %) T_TEB: Real (top exchange base);(unit: cmol/kg) T_CACO3: Real (top carbonate or lime content) (unit: % weight) T_CASO4: Real (top-level sulfate content);(unit: % weight) T_ESP: Real (top layer exchangeable sodium salt);(unit: %) T_ECE: Real (top-level conductivity).(unit: dS/m) S_GRAVEL: Real (percentage of bottom gravel volume);(unit: % vol.) S_SAND: Real (content of underlying sand);(unit: % wt.) S_SILT: Real (substratum silt content);(unit: % wt.) S_CLAY: Real (clay content in the bottom layer);(unit: % wt.) S_USDA_TEX: Real (USDA underlying soil texture classification);(unit: name) S_REF_BULK: Real (bulk density of underlying soil);(unit: kg/dm3.) S_OC: Real (bottom organic carbon content);(unit: % weight) S_PH_H2O: Real (base ph) (unit: -log(H+)) S_CEC_CLAY: Real (cation exchange capacity of the underlying cohesive soil);(unit: cmol/kg) S_CEC_SOIL: Real (cation exchange capacity of underlying soil) (unit: cmol/kg) S_BS: Real (underlying basic saturation);(unit: %) S_TEB: Real (underlying exchangeable base);(unit: cmol/kg) S_CACO3: Real (content of underlying carbonate or lime) (unit: % weight) S_CASO4: Real (substrate sulfate content);(unit: % weight) S_ESP: Real (underlying exchangeable sodium salt);(unit: %) S_ECE: Real (underlying conductivity).(unit: dS/m) This database is divided into two layers, in which the top layer (T) has a soil thickness of (0-30cm) and the bottom layer (S) has a soil thickness of (30-100cm).。 Refer to the instructions for other attribute values HWSD1.2_documentation.pdf，The Harmonized World Soil Database (HWSD V1.2) Viewer-Chinese description andHWSD.mdb。
Food and Agriculture Organization of the United Nations（FAO）
The basic data set of remote sensing for ecological assets assessment of the Qinghai-Tibet Plateau includes the annual Fraction Vegetation Coverage (FVC), Net Primary Productivity (NPP) and Leaf Area Index (LAI) of the Qinghai-Tibet Plateau since 2000, and other ecological parameters based on remote sensing inversion. The FVC data are mainly developed from MODIS NDVI data. NPP estimation method based on algorithm of CASA model.
LIU Wenjun
The Tibetan Plateau Glacier Data –TPG2013 is a glacial coverage data on the Tibetan Plateau around 2013. 128 Landsat 8 Operational Land Imager (OLI) images were selected with 30-m spatial resolution, for comparability with previous and current glacier inventories. Besides, about 20 images acquired in 2014 were used to complete the full coverage of the TP. The most frequent year in this period was defined as the reference year for the mosaic image: i.e. 2013. Glacier outlines were digitized on-screen manually from the 2013 image mosaic, relying on false-colour image composites (RGB by bands 654), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. [To minimize the effects of snow or cloud cover on glacierized areas, high-resolution (30 m spatial resolution and 4-day repetition cycle) images were also used for reference in glacier delineation from the Chinese satellites HJ-1A and HJ-1B, which were launched on Sep.6th 2008. Both carried as payload two 4-band CCD cameras with swath width 700 km (360 km per camera). All HJ-1A/1B data in 2012, 2013 and 2014 (65 scenes, Fig.S1, Table S1) were from China Centre for Resources Satellite Data and Application (CRESDA; http://www.cresda.com/n16/n92006/n92066/n98627/index.html). Each scene was orthorectified with respect to the 30m-resolution digital elevation model (DEM) of the Shuttle Radar Topography Mission (SRTM) and Landsat images.] The delineated glacier outlines were compared with band-ratio (e.g. TM3/TM5) results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. Topographic maps from the 1970s and all available satellite images (including Google EarthTM imagery and HJ-1A/1B satellite data) were used as base reference data. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG2013. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG2013 if they were identifiable on images in all three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 3.9%.
YE Qinghua
These data contain two data files: GLOBELAND30 TILES (raw data) and TIBET_ GLOBELAND30_MOSAIC (mosaic data). The raw data were downloaded from the Global Land Cover Data website (GlobalLand3) (http://www.globallandcover.com) and cover the Tibetan Plateau and surrounding areas. The raw data were stored in frames, and for the convenience of using the data, we use Erdas software to splice and mosaic the raw data. The Global Land Cover Data (GlobalLand30) is the result of the “Global Land Cover Remote Sensing Mapping and Key Technology Research”, which is a key project of the National 863 Program. Using the American Landsat images (TM5, ETM+) and Chinese Environmental Disaster Reduction Satellite images (HJ-1), the data were extracted by a comprehensive method based on pixel classification-object extraction-knowledge checks. The data include 10 primary land cover types—cultivated land, forest, grassland, shrub, wetland, water body, tundra, man-made cover, bare land, glacier and permanent snow—without extracting secondary types. In terms of accuracy assessment, nine types and more than 150,000 test samples were evaluated. The overall accuracy of the GlobeLand30-2010 data is 80.33%. The Kappa indicator is 0.75. The GlobeLand30 data use the WGS84 coordinate system, UTM projection, and 6-degree banding, and the reference ellipsoid is the WGS 84 ellipsoid. According to different latitudes, the data are organized into two types of framing. In the regions of 60° north and south latitudes, the framing is carried out according to a size of 5° (latitude) × 6° (longitude); in the regions of 60° to 80° north and south latitudes, the framing is carried out according to a size of 5° (latitude) × 12° (longitude). The framing is projected according to the central meridian of the odd 6° band. GLOBELAND30 TILES: The original, unprocessed raw data are retained. TIBET_ GLOBELAND30_MOSAIC: The Erdas software is used to mosaic the raw data. The parameter settings use the default value of the raw data to retain the original, and the accuracy is consistent with that of the downloading site.
CHEN Jun
This dataset is derived from the paper: Ding, J., Wang, T., Piao, S., Smith, P., Zhang, G., Yan, Z., Ren, S., Liu, D., Wang, S., Chen, S., Dai, F., He, J., Li, Y., Liu, Y., Mao, J., Arain, A., Tian, H., Shi, X., Yang, Y., Zeng, N., & Zhao, L. (2019). The paleoclimatic footprint in the soil carbon stock of the Tibetan permafrost region. Nature Communications, 10(1), 4195. doi:10.1038/s41467-019-12214-5. This data contains R code and a new estimate of Tibetan soil carbon pool to 3 m depth, at a 0.1° spatial resolution. Previous assessments of the Tibetan soil carbon pools have relied on a collection of predictors based only on modern climate and remote sensing-based vegetation features. Here, researchers have merged modern climate and remote sensing-based methods common in previous estimates, with paleoclimate, landform and soil geochemical properties in multiple machine learning algorithms, to make a new estimate of the permafrost soil carbon pool to 3 m depth over the Tibetan Plateau, and find that the stock (38.9-34.2 Pg C) is triple that predicted by ecosystem models (11.5 ± 4.2 Pg C), which use pre-industrial climate to initialize the soil carbon pool. This study provides evidence that illustrates, for the first time, the bias caused by the lack of paleoclimate information in ecosystem models. The data contains the following fields: Longitude (°E) Latitude (°N) SOCD (0-30cm) (kg C m-2) SOCD (0-300cm) (kg C m-2) GridArea (k㎡) 3mCstcok (10^6 kg C)
DING Jinzhi, WANG Tao
Photosynthetic effective radiation absorption coefficient photosynthetically active radiation component is an important biophysical parameter. It is an important land characteristic parameter of ecosystem function model, crop growth model, net primary productivity model, atmosphere model, biogeochemical model and ecological model, and is an ideal parameter for estimating vegetation biomass. The data set contains the data of photosynthetically active radiation absorption coefficient in Qinghai Tibet Plateau, with spatial resolution of 500m, temporal resolution of 8D, and time coverage of 2000, 2005, 2010 and 2015. The data source is MODIS Lai / FPAR product data mod15a2h (C6) on NASA website. The data are of great significance to the analysis of vegetation ecological environment in the Qinghai Tibet Plateau.
FANG Huajun, Ranga Myneni
This data set contains the statistical information of natural disasters in Qinghai Tibet Plateau in the past 50 years (1950-2002), including drought, snow disaster, frost disaster, hail, flood, wind disaster, lightning disaster, cold wave and strong cooling, low temperature and freezing damage, gale sandstorm, insect disaster, rodent damage and other meteorological disasters. Qinghai and Tibet are the main parts of the Qinghai Tibet Plateau. The Qinghai Tibet Plateau is one of the Centers for the formation and evolution of biological species in China. It is also a sensitive area and fragile zone for the international scientific and technological circles to study climate and ecological environment changes. Its complex terrain conditions, high altitude and severe climate conditions determine that the ecological environment is very fragile, It has become the most frequent area of natural disasters in China. The data were extracted from "China Meteorological Disaster Canon · Qinghai volume" and "China Meteorological Disaster Canon · Tibet Volume", which were manually input, summarized and proofread.
Statistical Bureau Statistical Bureau
This study takes the land resources in the Qinghai-Tibet Plateau as the evaluation object, and clarifies the current situation in the region suitable for agriculture, forestry, animal husbandry production and the quantity, quality and distribution of the reserve land resources. Through field investigations, collect relevant data from the study area, and combine relevant literature and expert experience to determine the evaluation factors (altitude, slope, annual precipitation, accumulated temperature, sunshine hours, soil effective depth, texture, erosion, vegetation type, NDVI). The grading and standardization are carried out, and the weights of each evaluation factor are determined by principal component analysis. The weighted index and model are used to determine the total score of the evaluation unit. Finally, the ArcGis natural discontinuity classification method is used to obtain the Qingshang Plateau. And the grades of farmland, forestry and grassland suitability drawings of the Qinghai-Tibet Plateau with a resolution of 90m were given. Finally, the results are verified and analyzed.
YAO Minglei
The data set records the number of employees of other units in Qinghai Province by industry and region at the end of the year, and the data is divided by the number of employees of other units by industry and region at the end of the year. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 8 data tables Number of employees in other units by industry at the end of the year 1995-2001.xls Number of employees in other units by industry at the end of the year 1995-2002.xls Number of employees of other units by industry at the end of 2003.xls Number of employees of other units by industry at the end of 2004.xls Number of employees of other units by industry at the end of 2005.xls Number of employees in other units by industry and region at the end of 2006.xls Number of employees in other units by industry and region at the end of 2007.xls The number of employees of other units by industry and region at the end of 2008.xls. The data table structure is similar. For example, there are 10 fields in the 2005 data table of the number of employees of other units by industry and region at the end of the year Field 1: Project Field 2: province total Field 3: Xining City Field 4: Haidong region Field 5: Haibei Prefecture Field 6: huangnanzhou Field 7: Hainan Field 8: Golog Field 9: Yushu prefecture Field 10: Haixi
Qinghai Provincial Bureau of Statistics
1) The data content includes three stages of soil erosion intensity in Qinghai-Tibet Plateau in 1992, 2005 and 2015m the grid resolution is 300m.2) The data of soil erosion intensity are obtained by using the Chinese soil erosion prediction model (CSLE). The formula of soil erosion prediction model includes rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover and biological measure factor, engineering measure factor and tillage measure factor. Rainfall erodibility factors are calculated from the daily rainfall data by the US Climate Prdiction Center (CPC); soil erodibility factors, engineering measures factors and tillage measures factors are obtained from the first water conservancy census data; slope length factors and slope factors are obtained by resampling after calculating 30 m elevation data; vegetation coverage and biological measures factors are obtained by combining fractional vegetation cover with land use data and rainfall erodibility proportionometer. The fractional vegetation cover is calculated by MODIS vegetation index products through pixel dichotomy. 3) Compared with the data of soil erosion intensity in the same region in the same year, there is no significant difference and the data quality is good.4) the data of soil erosion intensity is of great significance for studying the present situation of soil erosion in Pan third polar 65 countries and better carrying out the development policy of the area along the way.
ZHANG Wenbo
1)The data content includes three stages of soil erosion intensity in Qinghai-Tibet Plateau in 1992, 2005 and 2015, and the grid resolution is 300m. 2) China soil erosion prediction model (CSLE) was used to calculate the soil erosion amount of more than 4,000 investigation units on the Qinghai-Tibet Plateau. Soil erosion was interpolated according to land use on Qinghai-Tibet Plateau. According to the soil erosion classification standard, the soil erosion intensity map of Qinghai-Tibet Plateau was obtained. 3) By comparing the differences of three-stage soil erosion intensity data, it conforms to the actual change law and the data quality is good. 4) The data of soil erosion intensity are of great significance to the study of soil erosion in the Qinghai-Tibet Plateau and the sustainable development of local ecosystems. In the attribute table, "Value" represents the erosion intensity level, from 1 to 6, the value represents slight, mild, moderate, intense, extremely intense and severe. "BL" represents the percentage of echa erosion intensity in the total area.
ZHANG Wenbo
The Tibetan Plateau Glacial Data -TPG1976 is a glacial coverage data on the Tibetan Plateau in the 1970s. It was generated by manual interpretation from Landsat MSS multispectral image data. The temporal coverage was mainly from 1972 to 1979 by 60 m spatial resolution. It involved 205 scenes of Landsat MSS/TM. There were 189 scenes（92% coverage on TP）in 1972-79，including 116 scenes in 1976/77 (61% of all the collected satellite data).As high quality of MSS data is not accessible due to cloud and snow effects in the South-east Tibetan Plateau, earlier Landsat TM data was collected for usage, including 14 scenes of 1980s（1981,1986-89,which covers 6.5% of TP） and 2 scenes in 1994（by 1.5% coverage on TP）.Among all satellite data，77% was collected in winter with the minimum effects of cloud and seasonal snow. The most frequent year in this period was defined as the reference year for the mosaic image: i.e. 1976. Glacier outlines were digitized on-screen manually from the 1976 image mosaic, relying on false-colour image composites (MSS: red, green and blue (RGB) represented by bands 321; TM: RGB by bands 543), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. The delineated glacier outlines were compared with band-ratio results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG1976. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG1976 if they were identifiable on images in all three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 6.4% due to the 60 m spatial resolution images.
YE Qinghua, WU Yuwei
The surface air temperature dataset of the Tibetan Plateau is obtained by downscaling the China regional surface meteorological feature dataset (CRSMFD). It contains the daily mean surface air temperature and 3-hourly instantaneous surface air temperature. This dataset has a spatial resolution of 0.01°. Its time range for surface air temperature dataset is from 1979 to 2018. Spatial dimension of data: 73°E-106°E, 23°N-40°N. The surface air temperature with a 0.01° can serve as an important input for the modeling of land surface processes, such as surface evapotranspiration estimation, agricultural monitoring, and climate change analysis.
DING Lirong, ZHOU Ji, WANG Wei
There are two types of aerosol data in the Tibetan Plateau. Aerosol type data products are the results of aerosol type data fusion by using Meera 2 assimilation data and active satellite CALIPSO products through a series of data preprocessing, quality control, statistical analysis and comparative analysis. The key of the algorithm is to judge the CALIPSO aerosol type. According to CALIPSO aerosol types and quality control, and referring to merra 2 aerosol types, the final aerosol type data (12 kinds) and quality control results were obtained. Considering the vertical and spatial distribution of aerosols, it has high spatial resolution (0.625 ° × 0.5 °) and temporal resolution (month). Aerosol optical depth (AOD) is a visible band remote sensing inversion method developed by ourselves, combined with merra-2 model data and NASA's official product mod04. The data coverage time is from 2000 to 2019, with daily temporal resolution and spatial resolution of 0.1 degree. The retrieval method mainly uses the self-developed APRs algorithm to retrieve the aerosol optical depth over the ice and snow. The algorithm takes into account the BRDF characteristics of the ice and snow surface, and is suitable for the inversion of aerosol optical thickness on the ice and snow. The results show that the relative deviation of the data is less than 35%, which can effectively improve the coverage and accuracy of the polar AOD.
GUANG Jie, ZHAO Chuanfeng
This data set contains sequence data of the number variation of livestock in the major cities and counties of the Tibetan Plateau from 1970 to 2006. It is used to study the social and economic changes of the Tibetan Plateau. The table has ten fields. Field 1: Year Interpretation: Year of the data Field 2: Province Interpretation: The province from which the data were obtained Field 3: City/Prefecture Interpretation: The city or prefecture from which the data were obtained Field 4: County Interpretation: The name of the county Field 5: Large livestock (10,000) Interpretation: The number of large livestock such as cattle, horses, mules, donkeys, and camels. Field 6: Cattle herd (10,000) Interpretation: Number of cattle Field 7: Equine animals(10,000) Interpretation: The number of equine animals such as horses, mules and donkeys. Field 8: Horses (10,000) Interpretation: The number of horses Field 9: Sheep (10,000) Interpretation: The number of sheep Field 10: Data Sources Interpretation: Source of Data The data come from the statistical yearbook and county annals. Some are listed as follows. [1] Gansu Yearbook Editorial Committee. Gansu Yearbook [J]. Beijing: China Statistics Press, 1984, 1988-2009 [2] Statistical Bureau of Yunnan Province. Yunnan Statistical Yearbook [J]. Beijing: China Statistics Press, 1988-2009 [3] Statistical Bureau of Sichuan Province, Sichuan Survey Team. Sichuan Statistical Yearbook [J]. Beijing: China Statistics Press, 1987-1991, 1996-2009 [4] Statistical Bureau of Xinjiang Uighur Autonomous Region . Xinjiang Statistical Yearbook [J]. Beijing: China Statistics Press, 1989-1996, 1998-2009 [5] Statistical Bureau of Tibetan Autonomous Region. Tibet Statistical Yearbook [J]. Beijing: China Statistics Press, 1986-2009 [6] Statistical Bureau of Qinghai Province. Qinghai Statistical Yearbook [J]. Beijing: China Statistics Press, 1986-1994, 1996-2008. [7] County Annals Editorial Committee of Huzhu Tu Autonomous County. County Annals of Huzhu Tu Autonomous County [J]. Qinghai: Qinghai People's Publishing House, 1993 [8] Haiyan County Annals Editorial Committee. Haiyan County Annals[J]. Gansu: Gansu Cultural Publishing House, 1994 [9] Menyuan County Annals Editorial Committee. Menyuan County Annals[J]. Gansu: Gansu People's Publishing House, 1993 [10] Guinan County Annals Editorial Committee. Guinan County Annals [J]. Shanxi: Shanxi People's Publishing House, 1996 [11] Guide County Annals Editorial Committee. Guide County Annals[J]. Shanxi: Shanxi People's Publishing House, 1995 [12] Jianzha County Annals Editorial Committee. Jianzha County Annals [J]. Gansu: Gansu People's Publishing House, 2003 [13] Dari County Annals Editorial Committee. Dari County Annals [J]. Shanxi: Shanxi People's Publishing House, 1993 [14] Golmud City Annals Editorial Committee. Golmud City Annals [J]. Beijing: Fangzhi Publishing House, 2005 [15] Delingha City Annals Editorial Committee. Delingha City Annals [J]. Beijing: Fangzhi Publishing House, 2004 [16] Tianjun County Annals Editorial Committee. Tianjun County Annals [J]. Gansu: Gansu Cultural Publishing House, 1995 [17] Naidong County Annals Editorial Committee. Naidong County Annals [J]. Beijing: China Tibetology Press, 2006 [18] Gulang County Annals Editorial Committee. Gulang County Annals [J]. Gansu: Gansu People's Publishing House, 1996 [19] County Annals Editorial Committee of Akesai Kazak Autonomous County. County Annals of Akesai Kazakh Autonomous County [J]. Gansu: Gansu People's Publishing House, 1993 [20] Minxian County Annals Editorial Committee. Minxian County Annals [J]. Gansu: Gansu People's Publishing House, 1995 [21] Dangchang County Annals Editorial Committee. Dangchang County Annals [J]. Gansu: Gansu Cultural Publishing House, 1995 [22] Dangchang County Annals Editorial Committee. Dangchang County Annals(Sequel) (1985-2005) [J]. Gansu: Gansu Cultural Publishing House, 2006 [23] Wenxian County Annals Editorial Committee. Wenxian County Annals[J]. Gansu: Gansu Cultural Publishing House, 1997 [24] Kangle County Annals Editorial Committee. Kangle County Annals [J]. Shanghai: Sanlian Bookstore. 1995 [25] County Annals Editorial Committee of Jishishan (Baoan, Dongxiang, Sala) Autonomous County. County Annals of Jishishan (Baoan, Dongxiang, Sala) Autonomous County[J], Gansu: Gansu Cultural Publishing House, 1998 [26] Luqu County Annals Editorial Committee. Luqu County Annals [J]. Gansu: Gansu People's Publishing House, 2006 [27] Zhouqu County Annals Editorial Committee. Zhouqu County Annals [J]. Shanghai: Sanlian Bookstore. 1996 [28] Xiahe County Annals Editorial Committee. Xiahe County Annals [J]. 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National Bureau of Statics of China
The matching data of water and soil resources in the Qinghai Tibet Plateau, the potential evapotranspiration data calculated by Penman formula from the site meteorological data (2008-2016, national meteorological data sharing network), the evapotranspiration under the existing land use according to the influence coefficient of underlying surface, and the rainfall data obtained by interpolation from the site rainfall data in the meteorological data, are used to calculate the evapotranspiration under the existing land use according to the different land types of land use According to the difference, the matching coefficient of water and soil resources is obtained. The difference between the actual rainfall and the water demand under the existing land use conditions reflects the matching of water and soil resources. The larger the value is, the better the matching is. The spatial distribution of the matching of soil and water resources can pave the way for further understanding of the agricultural and animal husbandry resources in the Qinghai Tibet Plateau.
DONG Lingxiao
This dataset is the spatial distribution map of the marshes in the source region of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30 m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.
WANG Guangjun
